Update app.py

app.py

@@ -2,57 +2,46 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import tensorflow as tf
-from sklearn.preprocessing import StandardScaler, LabelEncoder
+import joblib
 
-# Load the model
+# Load trained model
 model = tf.keras.models.load_model("banking_model.keras")
 
-# Function to preprocess input data
-def preprocess_input(input_data, label_encoders, scaler):
-    # Convert input data to DataFrame
-    input_df = pd.DataFrame([input_data])
-    
-    # Encode categorical variables
-    for col in label_encoders:
-        input_df[col] = label_encoders[col].transform(input_df[col])
-    
-    # Scale numerical variables
-    numerical_columns = input_df.select_dtypes(include=["int64", "float64"]).columns
-    input_df[numerical_columns] = scaler.transform(input_df[numerical_columns])
-    
-    return input_df
-
-# Streamlit app
-def main():
-    st.title("Banking Stage Classification")
-    
-    # Input fields
-    st.sidebar.header("User Input Features")
-    
-    # Example feature inputs, adjust according to your actual features
-    credit_expiration = st.sidebar.number_input("Credit Expiration", min_value=0, value=0)
-    dpd = st.sidebar.number_input("DPD", min_value=0, value=0)
-    feature1 = st.sidebar.selectbox("Feature 1", options=["Yes", "No"])
-    feature2 = st.sidebar.selectbox("Feature 2", options=["Yes", "No"])
-    stage_last_month = st.sidebar.selectbox("Stage As Last Month", options=[1, 2, 3])
-    
-    # Prepare input data
-    input_data = {
-        'Credit Expiration': credit_expiration,
-        'DPD': dpd,
-        'Feature 1': feature1,
-        'Feature 2': feature2,
-        'Stage As Last Month': stage_last_month
-    }
-    
-    # Preprocess the input
-    processed_input = preprocess_input(input_data, label_encoders, scaler)
-    
-    # Make prediction
-    if st.sidebar.button("Predict"):
-        prediction = model.predict(processed_input)
-        predicted_stage = np.argmax(prediction, axis=1) + 1  # Adjust if necessary
-        st.success(f"Predicted Current Stage: {predicted_stage[0]}")
+# Load encoders and scaler
+label_encoders = joblib.load("label_encoders.pkl")
+scaler = joblib.load("scaler.pkl")
+
+# Define the input features
+feature_names = [
+    # Add all the feature column names used in training
+]
+
+st.title("Classification Prediction App")
+
+# Create input fields for user input
+user_input = {}
+for feature in feature_names:
+    if feature in label_encoders:  # If it's a categorical feature
+        options = list(label_encoders[feature].classes_)
+        user_input[feature] = st.selectbox(f"Select {feature}", options)
+    else:  # If it's a numerical feature
+        user_input[feature] = st.number_input(f"Enter {feature}", value=0.0)
+
+# Convert input to DataFrame
+input_df = pd.DataFrame([user_input])
+
+# Apply encoding & scaling
+for col, encoder in label_encoders.items():
+    input_df[col] = encoder.transform(input_df[col])
+
+input_df[feature_names] = scaler.transform(input_df[feature_names])
+
+# Predict when user clicks button
+if st.button("Predict"):
+    prediction = model.predict(input_df)
+    predicted_stage = np.argmax(prediction)
+    st.success(f"Predicted Stage: {predicted_stage}")
+
 
 if __name__ == "__main__":
     main()